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Devbora29
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Mistral-7b-Prompt-Conditioned

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The dataset generation failed
Error code:   DatasetGenerationError
Exception:    CastError
Message:      Couldn't cast
model_name: string
num_layers: int64
quant_options: list<item: int64>
  child 0, item: int64
hardware_practical: bool
protected_layers: list<item: int64>
  child 0, item: int64
policy_names: list<item: string>
  child 0, item: string
num_policies_per_prompt: int64
feature_type: string
embedding_dim: int64
embedding_target_dim: int64
projection_type: string
projection_seed: int64
scalar_features: list<item: string>
  child 0, item: string
conditioning_variables: list<item: string>
  child 0, item: string
alpha_sampling: string
alpha_range: list<item: double>
  child 0, item: double
alpha_anchors: list<item: double>
  child 0, item: double
sensitivity_keys: list<item: string>
  child 0, item: string
quality_metric: string
score_formula: string
num_prompts: int64
num_entries: int64
num_sources: int64
total_dpo_pairs: int64
to
{'source': Value('string'), 'chunk_idx': Value('int64'), 'prompt_features': {'num_tokens': Value('int64'), 'embedding': List(Value('float64')), 'alpha': Value('float64')}, 'baseline_ppl': Value('float64'), 'layer_sensitivity': {'0': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '1': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '2': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '3': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '4': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '5': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '6': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '7': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '8': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), '
...
lue('float64'), 'int4_kl_div': Value('float64')}, '25': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '26': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '27': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '28': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '29': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '30': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '31': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}}, 'policies': List({'policy_idx': Value('int64'), 'policy_name': Value('string'), 'quant_config': List(Value('int64')), 'ppl': Value('float64'), 'ppl_delta': Value('float64'), 'kl_div': Value('float64'), 'cost_mb': Value('float64'), 'score': Value('float64'), 'rank': Value('int64')}), 'ranking': List(Value('int64')), 'dpo_pairs': List({'chosen_idx': Value('int64'), 'rejected_idx': Value('int64'), 'margin': Value('float64')})}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1821, in _prepare_split_single
                  num_examples, num_bytes = writer.finalize()
                                            ^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 781, in finalize
                  self.write_rows_on_file()
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 663, in write_rows_on_file
                  self._write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 773, in _write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              model_name: string
              num_layers: int64
              quant_options: list<item: int64>
                child 0, item: int64
              hardware_practical: bool
              protected_layers: list<item: int64>
                child 0, item: int64
              policy_names: list<item: string>
                child 0, item: string
              num_policies_per_prompt: int64
              feature_type: string
              embedding_dim: int64
              embedding_target_dim: int64
              projection_type: string
              projection_seed: int64
              scalar_features: list<item: string>
                child 0, item: string
              conditioning_variables: list<item: string>
                child 0, item: string
              alpha_sampling: string
              alpha_range: list<item: double>
                child 0, item: double
              alpha_anchors: list<item: double>
                child 0, item: double
              sensitivity_keys: list<item: string>
                child 0, item: string
              quality_metric: string
              score_formula: string
              num_prompts: int64
              num_entries: int64
              num_sources: int64
              total_dpo_pairs: int64
              to
              {'source': Value('string'), 'chunk_idx': Value('int64'), 'prompt_features': {'num_tokens': Value('int64'), 'embedding': List(Value('float64')), 'alpha': Value('float64')}, 'baseline_ppl': Value('float64'), 'layer_sensitivity': {'0': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '1': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '2': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '3': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '4': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '5': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '6': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '7': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '8': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), '
              ...
              lue('float64'), 'int4_kl_div': Value('float64')}, '25': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '26': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '27': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '28': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '29': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '30': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}, '31': {'int8_ppl_delta': Value('float64'), 'int8_kl_div': Value('float64'), 'int4_ppl_delta': Value('float64'), 'int4_kl_div': Value('float64')}}, 'policies': List({'policy_idx': Value('int64'), 'policy_name': Value('string'), 'quant_config': List(Value('int64')), 'ppl': Value('float64'), 'ppl_delta': Value('float64'), 'kl_div': Value('float64'), 'cost_mb': Value('float64'), 'score': Value('float64'), 'rank': Value('int64')}), 'ranking': List(Value('int64')), 'dpo_pairs': List({'chosen_idx': Value('int64'), 'rejected_idx': Value('int64'), 'margin': Value('float64')})}
              because column names don't match
              
              The above exception was the direct cause of the following exception:
              
              Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 882, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 943, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1646, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1832, in _prepare_split_single
                  raise DatasetGenerationError("An error occurred while generating the dataset") from e
              datasets.exceptions.DatasetGenerationError: An error occurred while generating the dataset

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source
string
chunk_idx
int64
prompt_features
dict
baseline_ppl
float64
layer_sensitivity
dict
policies
list
ranking
list
dpo_pairs
list
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 3, 5, 10, 11, 1, 9, 2, 4, 8, 6, 7, 0 ]
[ { "chosen_idx": 3, "rejected_idx": 1, "margin": 0.0451 }, { "chosen_idx": 3, "rejected_idx": 9, "margin": 0.0581 }, { "chosen_idx": 3, "rejected_idx": 2, "margin": 0.1077 }, { "chosen_idx": 3, "rejected_idx": 4, "margin": 0.1493 }, { "chosen_idx": ...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 3, 5, 11, 10, 1, 9, 4, 2, 8, 6, 7, 0 ]
[ { "chosen_idx": 3, "rejected_idx": 1, "margin": 0.0274 }, { "chosen_idx": 3, "rejected_idx": 9, "margin": 0.0867 }, { "chosen_idx": 3, "rejected_idx": 4, "margin": 0.139 }, { "chosen_idx": 3, "rejected_idx": 2, "margin": 0.1741 }, { "chosen_idx": 3...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 11, 1, 3, 5, 10, 4, 9, 8, 6, 2, 7, 0 ]
[ { "chosen_idx": 11, "rejected_idx": 10, "margin": 0.051 }, { "chosen_idx": 11, "rejected_idx": 4, "margin": 0.1305 }, { "chosen_idx": 11, "rejected_idx": 9, "margin": 0.1451 }, { "chosen_idx": 11, "rejected_idx": 8, "margin": 0.1882 }, { "chosen_id...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 11, 1, 3, 5, 10, 4, 9, 8, 6, 2, 7, 0 ]
[ { "chosen_idx": 11, "rejected_idx": 10, "margin": 0.051 }, { "chosen_idx": 11, "rejected_idx": 4, "margin": 0.1305 }, { "chosen_idx": 11, "rejected_idx": 9, "margin": 0.1451 }, { "chosen_idx": 11, "rejected_idx": 8, "margin": 0.1882 }, { "chosen_id...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 1, 11, 3, 5, 8, 4, 10, 6, 9, 7, 0, 2 ]
[ { "chosen_idx": 1, "rejected_idx": 11, "margin": 0.038 }, { "chosen_idx": 1, "rejected_idx": 3, "margin": 0.0742 }, { "chosen_idx": 1, "rejected_idx": 5, "margin": 0.0742 }, { "chosen_idx": 1, "rejected_idx": 8, "margin": 0.1423 }, { "chosen_idx": ...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 1, 11, 3, 5, 8, 4, 6, 10, 7, 9, 0, 2 ]
[ { "chosen_idx": 1, "rejected_idx": 11, "margin": 0.0589 }, { "chosen_idx": 1, "rejected_idx": 3, "margin": 0.1072 }, { "chosen_idx": 1, "rejected_idx": 5, "margin": 0.1072 }, { "chosen_idx": 1, "rejected_idx": 8, "margin": 0.1238 }, { "chosen_idx":...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 1, 11, 8, 6, 3, 5, 4, 7, 10, 0, 9, 2 ]
[ { "chosen_idx": 1, "rejected_idx": 11, "margin": 0.088 }, { "chosen_idx": 1, "rejected_idx": 8, "margin": 0.098 }, { "chosen_idx": 1, "rejected_idx": 6, "margin": 0.1385 }, { "chosen_idx": 1, "rejected_idx": 3, "margin": 0.153 }, { "chosen_idx": 1,...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
{ "0": { "int8_ppl_delta": 0, "int8_kl_div": 0, "int4_ppl_delta": 0, "int4_kl_div": 0 }, "1": { "int8_ppl_delta": -0.0085, "int8_kl_div": 0.000265, "int4_ppl_delta": 0.1589, "int4_kl_div": 0.013992 }, "2": { "int8_ppl_delta": -0.0007, "int8_kl_div": -0.000004, "int4...
[ { "policy_idx": 0, "policy_name": "all_fp16", "quant_config": [ 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, ...
[ 1, 8, 6, 11, 4, 3, 5, 0, 7, 10, 9, 2 ]
[ { "chosen_idx": 1, "rejected_idx": 8, "margin": 0.07 }, { "chosen_idx": 1, "rejected_idx": 6, "margin": 0.0979 }, { "chosen_idx": 1, "rejected_idx": 11, "margin": 0.1196 }, { "chosen_idx": 1, "rejected_idx": 4, "margin": 0.1808 }, { "chosen_idx": 1...
conversation
8
{ "num_tokens": 512, "embedding": [ -0.0058852364, -0.0014896091, 0.0019911239, -0.001642098, -0.0023522491, -0.0012508472, -0.002760547, 0.0024613163, 0.0086905472, 0.0011061176, -0.0020369878, 0.0001165027, 0.0000470418, 0.0023281793, 0.0053199125, 0...
6.930053
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